Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder

A fractional-based compressed auto-encoder architecture has been introduced to solve the problem of denoising electroencephalogram (EEG) signals. The architecture makes use of fractional calculus to calculate the gradients during the backpropagation process, as a result of which a new hyper-paramete...

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Bibliographic Details
Published in:arXiv.org
Main Authors: Nagar, Subham, Kumar, Ahlad, Swamy, M N S
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 16.02.2021
Subjects:
Algorithms
Back propagation
Back propagation networks
Coders
Compression ratio
Compression tests
Computer architecture
Electroencephalography
Electronic devices
Fractional calculus
Neural networks
Noise reduction
Singular value decomposition
ISSN:2331-8422
Online Access:Get full text
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Summary:A fractional-based compressed auto-encoder architecture has been introduced to solve the problem of denoising electroencephalogram (EEG) signals. The architecture makes use of fractional calculus to calculate the gradients during the backpropagation process, as a result of which a new hyper-parameter in the form of fractional order (\(\alpha\)) has been introduced which can be tuned to get the best denoising performance. Additionally, to avoid substantial use of memory resources, the model makes use of orthogonal features in the form of Tchebichef moments as input. The orthogonal features have been used in achieving compression at the input stage. Considering the growing use of low energy devices, compression of neural networks becomes imperative. Here, the auto-encoder's weights are compressed using the randomized singular value decomposition (RSVD) algorithm during training while evaluation is performed using various compression ratios. The experimental results show that the proposed fractionally compressed architecture provides improved denoising results on the standard datasets when compared with the existing methods.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
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ISSN:2331-8422
DOI:10.48550/arxiv.2102.08083